system

Abstract

The system according to this embodiment aims to analyze photographs of plants taken by the user and provide information about those plants. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a provision unit, and a notification unit. The collection unit collects photographs of plants taken by the user. The analysis unit analyzes the photographs collected by the collection unit and identifies information such as the name of the plant, growth conditions, and cultivation methods. The provision unit provides the user with the information identified by the analysis unit. The notification unit provides seasonal plant care advice and notifications of the optimal harvest time for vegetables.

Description

Technical Field

[0001] The technology of the present disclosure relates to a system.

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, specialized knowledge is required to obtain information such as the names of plants and how to grow them, and there is a problem that it is difficult for general users to collect information.

[0005] The system according to the embodiment aims to analyze a photo of a plant taken by a user and provide information about the plant. A

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a provision unit, and a notification unit. The collection unit collects photographs of plants taken by the user. The analysis unit analyzes the photographs collected by the collection unit and identifies information such as the plant's name, growth conditions, and cultivation methods. The provision unit provides the user with the information identified by the analysis unit. The notification unit provides seasonal plant care advice and notifications of the optimal harvest time for vegetables. [Effects of the Invention]

[0007] The system according to this embodiment can analyze a photograph of a plant taken by the user and provide information about the plant. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server. <00**********]] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor ​​​The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The plant knowledge agent according to an embodiment of the present invention is a system that uses image analysis AI to provide plant names, growth conditions, cultivation methods, and other information in real time. This system allows users to instantly obtain detailed information simply by taking a picture of a plant with their smartphone or camera. Specifically, it consists of the following steps: First, the user takes a picture of a plant with their smartphone or camera. Next, the image analysis AI analyzes the picture and provides detailed information such as the plant's name, growth conditions, and cultivation methods. Furthermore, it also provides seasonal plant management advice and notifications of the optimal harvest time for vegetables. This is expected to improve the user's success rate in plant cultivation, extend plant lifespan through early detection of plant health issues, and reduce food costs by maximizing vegetable yields. For example, a user can learn the name and cultivation methods of a flower they find in their garden simply by taking a picture of it. The image analysis AI analyzes the characteristics of the plant in the photograph and identifies information such as the plant's name, growth conditions, and cultivation methods. For example, it analyzes leaf shape and color, flower shape, etc., to identify a specific plant. Once the analysis is complete, detailed information is provided to the user. For example, the plant's name, growth conditions, and cultivation methods are displayed. Furthermore, the system provides seasonal plant care advice and notifications about the optimal harvest time for vegetables. This allows users to receive specific advice on plant cultivation. This system improves users' success rates in plant cultivation. For example, knowing the appropriate timing for watering and fertilizing makes it easier to maintain the health of plants. It can also extend the lifespan of plants by enabling early detection of plant health problems. For example, early detection of signs of disease or pests and taking appropriate measures can extend the life of plants. In addition, it can reduce food costs by maximizing vegetable yields. For example, knowing the optimal harvest time allows users to maximize the quality of vegetables and increase yields. This can lead to reduced food costs for users who enjoy home gardening. In this way, the plant knowledge agent supports users' plant cultivation and promotes a deeper understanding and interaction with plants by providing plant names, growth conditions, and cultivation methods in real time using image analysis AI.This allows the plant knowledge agent to support users in growing plants and promote a deeper understanding and interaction with them.

[0029] The plant knowledge agent according to this embodiment comprises a collection unit, an analysis unit, a provision unit, and a notification unit. The collection unit collects photographs of plants taken by the user. The collection unit can, for example, collect photographs of plants taken by the user with a smartphone or camera. The collection unit can, for example, determine the name and cultivation method of a flower simply by the user taking a photograph of a flower found in their garden. The collection unit can, for example, enable the user to take a photograph of a plant without any special operation; they just need to take a picture. The analysis unit analyzes the photographs collected by the collection unit to identify information such as the plant's name, growth conditions, and cultivation method. The analysis unit analyzes the characteristics of the plants in the collected photographs, for example, using image analysis AI. The analysis unit analyzes the shape and color of the leaves, the shape of the flowers, etc., to identify a specific plant. The analysis unit, for example, uses image analysis AI to analyze the photographs and identifies information such as the plant's name, growth conditions, and cultivation method. The provision unit provides the user with the information identified by the analysis unit. The provision unit provides detailed information to the user, for example, once the analysis is complete. The information provision unit displays, for example, the name of the plant, its growth conditions, and how to cultivate it. The information provision unit allows users to receive specific advice on plant cultivation. The notification unit provides seasonal plant care advice and notifications about the optimal harvest time for vegetables. The notification unit provides, for example, seasonal plant care advice. The notification unit provides, for example, notifications about the optimal harvest time for vegetables. The notification unit allows users to know the appropriate timing for watering and fertilizing. Thus, the plant knowledge agent according to this embodiment can support plant cultivation by collecting, analyzing, providing information on, and notifying users of plant photos taken by the user.

[0030] The data collection unit collects plant photographs taken by users. For example, it can collect plant photographs taken by users with their smartphones or cameras. Specifically, users simply need to take pictures of plants they find in their gardens, parks, or indoors and upload those photos through the application. The data collection unit sends these photos to a cloud server and stores them in a central database. Users do not need to perform any special operations; they just need to take photos and upload them to the application, and the system will automatically collect them. The data collection unit also collects information on the resolution and shooting conditions of the photos, which the analysis unit uses as supplementary information to perform more accurate analysis. For example, metadata such as the date and time the photo was taken, location information, and lighting conditions are also collected. This allows the data collection unit to efficiently collect plant photographs taken in diverse environments and provide them to the analysis unit. Furthermore, the data collection unit has a function to automatically evaluate the quality of the photos taken by users and select only those suitable for analysis. This allows the analysis unit to perform analysis based on high-quality data, improving the accuracy and reliability of the entire system.

[0031] The analysis unit analyzes the photographs collected by the collection unit to identify information such as the plant's name, growth conditions, and cultivation methods. For example, the analysis unit uses image analysis AI to analyze the characteristics of the plants in the collected photographs. Specifically, the image analysis AI uses deep learning technology to extract features such as leaf shape, color, flower shape, and stem structure, and identifies the plant species based on these features. The analysis unit refers to a large, pre-trained plant database and compares it with the collected photographs to identify information such as the plant's name, growth conditions, and cultivation methods. For example, if the leaf shape matches a specific pattern, it identifies the plant as a specific species. The analysis unit can also analyze the plant's growth stage and health. For example, discoloration of the leaves may indicate nutrient deficiency or the effects of pests and diseases. This allows the analysis unit to identify detailed information from the user's photographs of plants and transmit it to the information provider. Furthermore, the analysis unit can continuously learn from the analysis results of the collected photographs, improving its analysis accuracy. This enables the analysis unit to always perform highly accurate analyses based on the latest information, providing users with reliable information.

[0032] The service provider provides users with information identified by the analysis unit. For example, once the analysis is complete, the service provider provides users with detailed information. Specifically, the name of the plant photographed by the user, its growth conditions, and how to care for it will be displayed on the application screen. The service provider provides detailed guidelines and tips so that users can receive specific advice on plant cultivation. For example, it will display information such as the frequency of watering, the appropriate sunlight conditions, and the type and amount of fertilizer needed. The service provider also provides an interface where users can input questions about plant cultivation and receive answers in real time. Furthermore, based on the analysis results, the service provider has a function that suggests related information and recommended plants that the user might be interested in. This allows users to deepen their knowledge of plant cultivation and grow plants more effectively. The service provider can collect user feedback and continuously improve the accuracy and content of the information it provides. This allows the service provider to always provide users with the latest and most optimal information and support their plant cultivation.

[0033] The notification unit provides seasonal plant care advice and notifications about the optimal harvest time for vegetables. For example, it provides seasonal plant care advice. Specifically, it notifies users of different care methods for each season, such as pruning new shoots and fertilizing methods in spring, watering frequency and sunlight management in summer, harvesting and pest and disease control in autumn, and cold protection measures in winter. The notification unit suggests the optimal care method according to the type of plant the user is growing and its growth stage. The notification unit also notifies users about the optimal harvest time for vegetables. For example, when vegetables such as tomatoes and cucumbers reach their optimal harvest time, it sends a notification to the user to let them know when to harvest. This allows the user to harvest vegetables in their optimal condition. The notification unit also has a reminder function so that users know the appropriate timing for watering and fertilizing. For example, it sends a notification to the user so that they do not forget to water and fertilize regularly. Furthermore, the notification unit can also provide care advice tailored to specific weather conditions based on weather information and weather forecasts. This allows the notification unit to receive timely information necessary for plant growth, enabling users to perform appropriate management to maintain plant health.

[0034] The collection unit can analyze the user's past plant photo collection history and select the optimal collection method. For example, the collection unit can analyze the frequency of plant photos taken by the user in the past and suggest the optimal collection timing. For example, the collection unit can analyze the types of plants photographed by the user in the past and suggest a collection method for specific plants. For example, the collection unit can suggest optimal shooting conditions (time of day, weather, etc.) based on the user's past collection history. In this way, the optimal collection method can be selected by analyzing the user's past collection history. Some or all of the above processing in the collection unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the collection unit can input the user's past collection history data into a generative AI and have the generative AI select the optimal collection method.

[0035] The collection unit can filter plant photos based on the user's current areas of interest and cultivation status. For example, the collection unit may prioritize collecting photos related to plants the user is currently growing. For example, the collection unit may filter photos based on the types of plants the user is interested in. For example, the collection unit may collect photos containing necessary information according to the user's cultivation status. In this way, by filtering photos based on the user's areas of interest and cultivation status, photos containing necessary information can be collected. Some or all of the above processing in the collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the collection unit can input the user's areas of interest and cultivation status data into a generative AI and have the generative AI perform the photo filtering.

[0036] The collection unit can prioritize the collection of highly relevant plant photos by considering the user's geographical location information when collecting plant photos. For example, the collection unit can prioritize the collection of plant photos that inhabit the user's current location based on that location. For example, the collection unit can analyze the user's past location information and collect relevant plant photos. For example, the collection unit can collect plant photos related to places the user has visited based on their travel history. In this way, by considering the user's geographical location information, the collection unit can prioritize the collection of highly relevant plant photos. Some or all of the above processing in the collection unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the collection unit can input the user's geographical location information data into a generative AI and have the generative AI perform the collection of highly relevant plant photos.

[0037] The collection unit can analyze a user's social media activity and collect relevant plant photos when collecting plant photos. For example, the collection unit can analyze plant photos shared by a user on social media and collect relevant plant photos. For example, the collection unit can analyze plant-related accounts followed by a user and collect relevant plant photos. For example, the collection unit can collect plant photos that a user has shown interest in on social media. In this way, relevant plant photos can be collected by analyzing a user's social media activity. Some or all of the above processing in the collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the collection unit can input the user's social media activity data into a generative AI and have the generative AI perform the collection of relevant plant photos.

[0038] The analysis unit can adjust the level of detail of the analysis based on the importance of the plant during the analysis. For example, the analysis unit performs a detailed analysis for important plants. For example, the analysis unit performs a basic analysis for common plants. For example, the analysis unit performs a special analysis for rare plants. In this way, by adjusting the level of detail of the analysis based on the importance of the plant, the necessary information can be provided. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input plant importance data into a generative AI and have the generative AI perform the adjustment of the level of detail of the analysis.

[0039] The analysis unit can apply different analysis algorithms depending on the plant category during analysis. For example, for flowering plants, the analysis unit applies an analysis algorithm that emphasizes the shape and color of the flowers. For example, for tree plants, the analysis unit applies an analysis algorithm that emphasizes the shape of the bark and leaves. For example, for vegetable plants, the analysis unit applies an analysis algorithm that emphasizes the growth stage and harvest time. By applying different analysis algorithms according to the plant category, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input plant category data into a generative AI and have the generative AI execute the application of different analysis algorithms.

[0040] The analysis unit can determine the priority of analysis based on the time of year the plants were photographed. For example, the analysis unit prioritizes the analysis of plants in their growth phase. For example, the analysis unit prioritizes the analysis of plants in their flowering phase. For example, the analysis unit prioritizes the analysis of plants in their harvesting phase. By determining the priority of analysis based on the time of year the plants were photographed, important information can be provided preferentially. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input plant photography time data into a generative AI and have the generative AI perform the determination of the analysis priority.

[0041] The analysis unit can improve the accuracy of its analysis by referring to relevant plant literature during the analysis. For example, the analysis unit can improve the accuracy of its analysis by referring to academic papers on plants. For example, the analysis unit can improve the accuracy of its analysis by referring to plant cultivation guides. For example, the analysis unit can improve the accuracy of its analysis by referring to specialized books on plants. In this way, the accuracy of the analysis is improved by referring to relevant plant literature. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input data on relevant plant literature into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0042] The information provider can select the optimal information provision method by referring to the user's past usage history when providing information. For example, the information provider may prioritize information provision methods previously used by the user. For example, the information provider may propose the optimal information provision method based on the user's past usage history. For example, the information provider may analyze the user's past usage history and select the optimal information provision method. This allows the optimal information provision method to be selected by referring to the user's past usage history. Some or all of the above processing in the information provider may be performed using, for example, a generation AI, or without using a generation AI. For example, the information provider can input the user's past usage history data into a generation AI and have the generation AI select the optimal information provision method.

[0043] The information provider can adjust the level of detail of the information provided based on the user's current skill level. For example, if the user is a beginner, the provider provides basic information. If the user is an intermediate user, the provider provides detailed information. If the user is an advanced user, the provider provides specialized information. By adjusting the level of detail of the information according to the user's skill level, more appropriate information can be provided. Some or all of the above processing in the information provider may be performed using, for example, a generative AI, or without a generative AI. For example, the information provider can input user skill level data into a generative AI and have the generative AI perform the adjustment of the level of detail of the information.

[0044] The information provider can provide optimal information by considering the user's geographical location when providing information. For example, the provider can provide information relevant to the user's region based on the user's current location. For example, the provider can analyze the user's past location information and provide relevant information. For example, the provider can provide information related to places visited based on the user's travel history. In this way, by considering the user's geographical location information, highly relevant information can be provided. Some or all of the above processing in the information provider may be performed using, for example, a generative AI, or without a generative AI. For example, the provider can input the user's geographical location data into a generative AI and have the generative AI perform the task of providing optimal information.

[0045] The information provider can analyze the user's social media activity and provide relevant information when providing information. For example, the provider can analyze information shared by the user on social media and provide relevant information. For example, the provider can analyze plant-related accounts followed by the user and provide relevant information. For example, the provider can provide information that the user has shown interest in on social media. In this way, relevant information can be provided by analyzing the user's social media activity. Some or all of the above processing in the information provider may be performed using, for example, a generative AI, or without a generative AI. For example, the provider can input the user's social media activity data into a generative AI and have the generative AI perform the provision of relevant information.

[0046] The notification unit can select the optimal notification method by referring to the user's past notification history when sending a notification. For example, the notification unit may prioritize notification methods that the user has received in the past. For example, the notification unit may suggest the optimal notification method based on the user's past notification history. For example, the notification unit may analyze the user's past notification history and select the optimal notification method. In this way, the optimal notification method can be selected by referring to the user's past notification history. Some or all of the above processing in the notification unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the notification unit may input the user's past notification history data into a generative AI and have the generative AI perform the selection of the optimal notification method.

[0047] The notification unit can customize notification content based on the user's current training status. For example, if the user is a beginner, the notification unit will notify them of basic training advice. If the user is an intermediate player, the notification unit will notify them of detailed training advice. If the user is an advanced player, the notification unit will notify them of expert training advice. This allows for more appropriate notifications by customizing the notification content according to the user's training status. Some or all of the above processing in the notification unit may be performed using, for example, a generative AI, or without a generative AI. For example, the notification unit can input the user's training status data into a generative AI and have the generative AI customize the notification content.

[0048] The notification unit can provide the most relevant notifications by considering the user's geographical location information. For example, the notification unit can provide notifications related to the user's current location. For example, the notification unit can provide notifications related to the user's past location information by analyzing it. For example, the notification unit can provide notifications related to places visited by the user based on their travel history. This allows for the provision of highly relevant notifications by considering the user's geographical location information. Some or all of the above processing in the notification unit may be performed using, for example, a generative AI, or without a generative AI. For example, the notification unit can input the user's geographical location information data into a generative AI and have the generative AI execute the most relevant notifications.

[0049] The notification unit can analyze the user's social media activity and send relevant notifications at the time of notification. For example, the notification unit can analyze information shared by the user on social media and send relevant notifications. For example, the notification unit can analyze plant-related accounts followed by the user and send relevant notifications. For example, the notification unit can send notifications based on information the user has shown interest in on social media. In this way, relevant notifications can be sent by analyzing the user's social media activity. Some or all of the above processing in the notification unit may be performed using, for example, a generative AI, or without a generative AI. For example, the notification unit can input the user's social media activity data into a generative AI and have the generative AI execute the relevant notifications.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The analysis unit can be enhanced to diagnose the health of plants when analyzing plant photographs. For example, it can detect changes in leaf color and shape to identify signs of disease or nutrient deficiency. The analysis unit can also monitor plant growth rate and detect abnormal growth patterns. For example, it can analyze spots and discoloration appearing on the plant surface to identify pest damage. By diagnosing the plant's health, it can suggest early countermeasures to the user.

[0052] The notification unit can send notifications to the user prompting specific actions according to the plant's growth stage. For example, it can notify the user when the plant has germinated, when it's time to water it, when it has entered its growth phase, when it needs to add fertilizer, and when it has entered its flowering phase, when it needs to care for the flowers. This allows the system to support the healthy growth of plants by prompting appropriate actions according to their growth stage.

[0053] The analysis unit can be enhanced with a function to predict plant growth when analyzing plant photographs. For example, it can predict future growth based on the current growth state and suggest appropriate cultivation methods. The analysis unit can also analyze the plant's growth rate and predict growth delays or abnormalities. For example, it can consider the plant's environmental conditions and suggest the optimal growth environment. By predicting plant growth, it can provide users with more effective cultivation methods.

[0054] The collection unit can adjust the timing of plant photo collection, taking into account the user's health condition. For example, if the user is unwell, the collection timing will be delayed. If the user is healthy, the collection unit will proactively suggest collection timings. If the user is tired, the collection unit will encourage collection at a time when they can relax. In this way, by adjusting the collection timing according to the user's health condition, plant photos can be collected without strain.

[0055] The analysis unit can be enhanced to perform plant component analysis when analyzing plant photographs. For example, it can analyze the components of plant leaves and flowers to identify nutritional value and medicinal components. The analysis unit can also analyze the components of plant roots and stems to evaluate soil conditions and nutrient balance. Furthermore, it can analyze the components of plant fruits to assess their suitability for consumption. By performing plant component analysis, the system can provide users with detailed information on how to use and cultivate plants.

[0056] The notification unit can be enhanced to notify users of important events related to plant growth. For example, it can notify users when a plant flowers. It can also notify users when a plant bears fruit, indicating the right time for harvest. Furthermore, it can notify users of early intervention when a plant becomes diseased. By notifying users of important events related to plant growth, the system can encourage timely action.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The collection unit collects photos of plants taken by the user. For example, it can collect photos of plants taken by the user with their smartphone or camera. By simply taking a picture of a flower the user finds in their garden, they can find out the name of the flower and how to grow it. No special operations are required; just take a picture. Step 2: The analysis unit analyzes the photos collected by the collection unit to identify information such as the plant's name, growth conditions, and cultivation methods. For example, it uses image analysis AI to analyze the characteristics of the plants shown in the collected photos. It analyzes the shape and color of the leaves, the shape of the flowers, etc., to identify specific plants. Step 3: The provisioning unit provides the user with the information identified by the analysis unit. For example, once the analysis is complete, the user is provided with detailed information. The plant's name, growth conditions, and cultivation methods are displayed, allowing the user to receive specific advice on plant cultivation. Step 4: The notification section provides seasonal plant care advice and notifications about the optimal harvest time for vegetables. For example, it provides seasonal plant care advice and notifications about the optimal harvest time for vegetables. Users can learn the appropriate timing for watering and fertilizing.

[0059] (Example of form 2) The plant knowledge agent according to an embodiment of the present invention is a system that uses image analysis AI to provide plant names, growth conditions, cultivation methods, and other information in real time. This system allows users to instantly obtain detailed information simply by taking a picture of a plant with their smartphone or camera. Specifically, it consists of the following steps: First, the user takes a picture of a plant with their smartphone or camera. Next, the image analysis AI analyzes the picture and provides detailed information such as the plant's name, growth conditions, and cultivation methods. Furthermore, it also provides seasonal plant management advice and notifications of the optimal harvest time for vegetables. This is expected to improve the user's success rate in plant cultivation, extend plant lifespan through early detection of plant health issues, and reduce food costs by maximizing vegetable yields. For example, a user can learn the name and cultivation methods of a flower they find in their garden simply by taking a picture of it. The image analysis AI analyzes the characteristics of the plant in the photograph and identifies information such as the plant's name, growth conditions, and cultivation methods. For example, it analyzes leaf shape and color, flower shape, etc., to identify a specific plant. Once the analysis is complete, detailed information is provided to the user. For example, the plant's name, growth conditions, and cultivation methods are displayed. Furthermore, the system provides seasonal plant care advice and notifications about the optimal harvest time for vegetables. This allows users to receive specific advice on plant cultivation. This system improves users' success rates in plant cultivation. For example, knowing the appropriate timing for watering and fertilizing makes it easier to maintain the health of plants. It can also extend the lifespan of plants by enabling early detection of plant health problems. For example, early detection of signs of disease or pests and taking appropriate measures can extend the life of plants. In addition, it can reduce food costs by maximizing vegetable yields. For example, knowing the optimal harvest time allows users to maximize the quality of vegetables and increase yields. This can lead to reduced food costs for users who enjoy home gardening. In this way, the plant knowledge agent supports users' plant cultivation and promotes a deeper understanding and interaction with plants by providing plant names, growth conditions, and cultivation methods in real time using image analysis AI.This allows the plant knowledge agent to support users in growing plants and promote a deeper understanding and interaction with them.

[0060] The plant knowledge agent according to this embodiment comprises a collection unit, an analysis unit, a provision unit, and a notification unit. The collection unit collects photographs of plants taken by the user. The collection unit can, for example, collect photographs of plants taken by the user with a smartphone or camera. The collection unit can, for example, determine the name and cultivation method of a flower simply by the user taking a photograph of a flower found in their garden. The collection unit can, for example, enable the user to take a photograph of a plant without any special operation; they just need to take a picture. The analysis unit analyzes the photographs collected by the collection unit to identify information such as the plant's name, growth conditions, and cultivation method. The analysis unit analyzes the characteristics of the plants in the collected photographs, for example, using image analysis AI. The analysis unit analyzes the shape and color of the leaves, the shape of the flowers, etc., to identify a specific plant. The analysis unit, for example, uses image analysis AI to analyze the photographs and identifies information such as the plant's name, growth conditions, and cultivation method. The provision unit provides the user with the information identified by the analysis unit. The provision unit provides detailed information to the user, for example, once the analysis is complete. The information provision unit displays, for example, the name of the plant, its growth conditions, and how to cultivate it. The information provision unit allows users to receive specific advice on plant cultivation. The notification unit provides seasonal plant care advice and notifications about the optimal harvest time for vegetables. The notification unit provides, for example, seasonal plant care advice. The notification unit provides, for example, notifications about the optimal harvest time for vegetables. The notification unit allows users to know the appropriate timing for watering and fertilizing. Thus, the plant knowledge agent according to this embodiment can support plant cultivation by collecting, analyzing, providing information on, and notifying users of plant photos taken by the user.

[0061] The data collection unit collects plant photographs taken by users. For example, it can collect plant photographs taken by users with their smartphones or cameras. Specifically, users simply need to take pictures of plants they find in their gardens, parks, or indoors and upload those photos through the application. The data collection unit sends these photos to a cloud server and stores them in a central database. Users do not need to perform any special operations; they just need to take photos and upload them to the application, and the system will automatically collect them. The data collection unit also collects information on the resolution and shooting conditions of the photos, which the analysis unit uses as supplementary information to perform more accurate analysis. For example, metadata such as the date and time the photo was taken, location information, and lighting conditions are also collected. This allows the data collection unit to efficiently collect plant photographs taken in diverse environments and provide them to the analysis unit. Furthermore, the data collection unit has a function to automatically evaluate the quality of the photos taken by users and select only those suitable for analysis. This allows the analysis unit to perform analysis based on high-quality data, improving the accuracy and reliability of the entire system.

[0062] The analysis unit analyzes the photographs collected by the collection unit to identify information such as the plant's name, growth conditions, and cultivation methods. For example, the analysis unit uses image analysis AI to analyze the characteristics of the plants in the collected photographs. Specifically, the image analysis AI uses deep learning technology to extract features such as leaf shape, color, flower shape, and stem structure, and identifies the plant species based on these features. The analysis unit refers to a large, pre-trained plant database and compares it with the collected photographs to identify information such as the plant's name, growth conditions, and cultivation methods. For example, if the leaf shape matches a specific pattern, it identifies the plant as a specific species. The analysis unit can also analyze the plant's growth stage and health. For example, discoloration of the leaves may indicate nutrient deficiency or the effects of pests and diseases. This allows the analysis unit to identify detailed information from the user's photographs of plants and transmit it to the information provider. Furthermore, the analysis unit can continuously learn from the analysis results of the collected photographs, improving its analysis accuracy. This enables the analysis unit to always perform highly accurate analyses based on the latest information, providing users with reliable information.

[0063] The service provider provides users with information identified by the analysis unit. For example, once the analysis is complete, the service provider provides users with detailed information. Specifically, the name of the plant photographed by the user, its growth conditions, and how to care for it will be displayed on the application screen. The service provider provides detailed guidelines and tips so that users can receive specific advice on plant cultivation. For example, it will display information such as the frequency of watering, the appropriate sunlight conditions, and the type and amount of fertilizer needed. The service provider also provides an interface where users can input questions about plant cultivation and receive answers in real time. Furthermore, based on the analysis results, the service provider has a function that suggests related information and recommended plants that the user might be interested in. This allows users to deepen their knowledge of plant cultivation and grow plants more effectively. The service provider can collect user feedback and continuously improve the accuracy and content of the information it provides. This allows the service provider to always provide users with the latest and most optimal information and support their plant cultivation.

[0064] The notification unit provides seasonal plant care advice and notifications about the optimal harvest time for vegetables. For example, it provides seasonal plant care advice. Specifically, it notifies users of different care methods for each season, such as pruning new shoots and fertilizing methods in spring, watering frequency and sunlight management in summer, harvesting and pest and disease control in autumn, and cold protection measures in winter. The notification unit suggests the optimal care method according to the type of plant the user is growing and its growth stage. The notification unit also notifies users about the optimal harvest time for vegetables. For example, when vegetables such as tomatoes and cucumbers reach their optimal harvest time, it sends a notification to the user to let them know when to harvest. This allows the user to harvest vegetables in their optimal condition. The notification unit also has a reminder function so that users know the appropriate timing for watering and fertilizing. For example, it sends a notification to the user so that they do not forget to water and fertilize regularly. Furthermore, the notification unit can also provide care advice tailored to specific weather conditions based on weather information and weather forecasts. This allows the notification unit to receive timely information necessary for plant growth, enabling users to perform appropriate management to maintain plant health.

[0065] The data collection unit can estimate the user's emotions and adjust the timing of plant photo collection based on the estimated emotions. For example, if the user is relaxed, the data collection unit suggests a good time to take a photo of a plant. If the user is busy, the data collection unit notifies the user of the optimal time to take a photo of a plant. If the user is excited, the data collection unit urges the user to take a photo of a plant immediately. By adjusting the timing of plant photo collection according to the user's emotions, photos can be collected at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0066] The collection unit can analyze the user's past plant photo collection history and select the optimal collection method. For example, the collection unit can analyze the frequency of plant photos taken by the user in the past and suggest the optimal collection timing. For example, the collection unit can analyze the types of plants photographed by the user in the past and suggest a collection method for specific plants. For example, the collection unit can suggest optimal shooting conditions (time of day, weather, etc.) based on the user's past collection history. In this way, the optimal collection method can be selected by analyzing the user's past collection history. Some or all of the above processing in the collection unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the collection unit can input the user's past collection history data into a generative AI and have the generative AI select the optimal collection method.

[0067] The collection unit can filter plant photos based on the user's current areas of interest and cultivation status. For example, the collection unit may prioritize collecting photos related to plants the user is currently growing. For example, the collection unit may filter photos based on the types of plants the user is interested in. For example, the collection unit may collect photos containing necessary information according to the user's cultivation status. In this way, by filtering photos based on the user's areas of interest and cultivation status, photos containing necessary information can be collected. Some or all of the above processing in the collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the collection unit can input the user's areas of interest and cultivation status data into a generative AI and have the generative AI perform the photo filtering.

[0068] The collection unit can estimate the user's emotions and determine the priority of plant photos to collect based on the estimated emotions. For example, if the user is relaxed, the collection unit will prioritize collecting photos of plants of interest. If the user is busy, the collection unit will prioritize collecting photos of important plants. If the user is excited, the collection unit will prioritize collecting photos of new plants. This allows for the priority collection of important photos by prioritizing plant photos according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0069] The collection unit can prioritize the collection of highly relevant plant photos by considering the user's geographical location information when collecting plant photos. For example, the collection unit can prioritize the collection of plant photos that inhabit the user's current location based on that location. For example, the collection unit can analyze the user's past location information and collect relevant plant photos. For example, the collection unit can collect plant photos related to places the user has visited based on their travel history. In this way, by considering the user's geographical location information, the collection unit can prioritize the collection of highly relevant plant photos. Some or all of the above processing in the collection unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the collection unit can input the user's geographical location information data into a generative AI and have the generative AI perform the collection of highly relevant plant photos.

[0070] The collection unit can analyze a user's social media activity and collect relevant plant photos when collecting plant photos. For example, the collection unit can analyze plant photos shared by a user on social media and collect relevant plant photos. For example, the collection unit can analyze plant-related accounts followed by a user and collect relevant plant photos. For example, the collection unit can collect plant photos that a user has shown interest in on social media. In this way, relevant plant photos can be collected by analyzing a user's social media activity. Some or all of the above processing in the collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the collection unit can input the user's social media activity data into a generative AI and have the generative AI perform the collection of relevant plant photos.

[0071] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis results based on the estimated emotions. For example, if the user is relaxed, the analysis unit provides detailed analysis results. If the user is busy, the analysis unit provides concise analysis results. If the user is excited, the analysis unit provides visually appealing analysis results. By adjusting the presentation of the analysis results according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0072] The analysis unit can adjust the level of detail of the analysis based on the importance of the plant during the analysis. For example, the analysis unit performs a detailed analysis for important plants. For example, the analysis unit performs a basic analysis for common plants. For example, the analysis unit performs a special analysis for rare plants. In this way, by adjusting the level of detail of the analysis based on the importance of the plant, the necessary information can be provided. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input plant importance data into a generative AI and have the generative AI perform the adjustment of the level of detail of the analysis.

[0073] The analysis unit can apply different analysis algorithms depending on the plant category during analysis. For example, for flowering plants, the analysis unit applies an analysis algorithm that emphasizes the shape and color of the flowers. For example, for tree plants, the analysis unit applies an analysis algorithm that emphasizes the shape of the bark and leaves. For example, for vegetable plants, the analysis unit applies an analysis algorithm that emphasizes the growth stage and harvest time. By applying different analysis algorithms according to the plant category, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input plant category data into a generative AI and have the generative AI execute the application of different analysis algorithms.

[0074] The analysis unit can estimate the user's emotions and adjust how the analysis results are displayed based on the estimated emotions. For example, if the user is relaxed, the analysis unit displays detailed analysis results. If the user is busy, the analysis unit displays concise analysis results. If the user is excited, the analysis unit displays visually appealing analysis results. By adjusting how the analysis results are displayed according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0075] The analysis unit can determine the priority of analysis based on the time of year the plants were photographed. For example, the analysis unit prioritizes the analysis of plants in their growth phase. For example, the analysis unit prioritizes the analysis of plants in their flowering phase. For example, the analysis unit prioritizes the analysis of plants in their harvesting phase. By determining the priority of analysis based on the time of year the plants were photographed, important information can be provided preferentially. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input plant photography time data into a generative AI and have the generative AI perform the determination of the analysis priority.

[0076] The analysis unit can improve the accuracy of its analysis by referring to relevant plant literature during the analysis. For example, the analysis unit can improve the accuracy of its analysis by referring to academic papers on plants. For example, the analysis unit can improve the accuracy of its analysis by referring to plant cultivation guides. For example, the analysis unit can improve the accuracy of its analysis by referring to specialized books on plants. In this way, the accuracy of the analysis is improved by referring to relevant plant literature. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input data on relevant plant literature into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0077] The information provider can estimate the user's emotions and adjust the method of information delivery based on the estimated emotions. For example, if the user is relaxed, the information provider can provide detailed information. If the user is busy, the information provider can provide concise information. If the user is excited, the information provider can provide visually appealing information. By adjusting the method of information delivery according to the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI or not using AI. For example, the information provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0078] The information provider can select the optimal information provision method by referring to the user's past usage history when providing information. For example, the information provider may prioritize information provision methods previously used by the user. For example, the information provider may propose the optimal information provision method based on the user's past usage history. For example, the information provider may analyze the user's past usage history and select the optimal information provision method. This allows the optimal information provision method to be selected by referring to the user's past usage history. Some or all of the above processing in the information provider may be performed using, for example, a generation AI, or without using a generation AI. For example, the information provider can input the user's past usage history data into a generation AI and have the generation AI select the optimal information provision method.

[0079] The information provider can adjust the level of detail of the information provided based on the user's current skill level. For example, if the user is a beginner, the provider provides basic information. If the user is an intermediate user, the provider provides detailed information. If the user is an advanced user, the provider provides specialized information. By adjusting the level of detail of the information according to the user's skill level, more appropriate information can be provided. Some or all of the above processing in the information provider may be performed using, for example, a generative AI, or without a generative AI. For example, the information provider can input user skill level data into a generative AI and have the generative AI perform the adjustment of the level of detail of the information.

[0080] The information provider can estimate the user's emotions and determine the priority of information delivery based on the estimated emotions. For example, if the user is relaxed, the information provider will prioritize providing information of interest. For example, if the user is busy, the information provider will prioritize providing important information. For example, if the user is excited, the information provider will prioritize providing new information. In this way, by determining the priority of information delivery according to the user's emotions, important information can be delivered preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the information provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0081] The information provider can provide optimal information by considering the user's geographical location when providing information. For example, the provider can provide information relevant to the user's region based on the user's current location. For example, the provider can analyze the user's past location information and provide relevant information. For example, the provider can provide information related to places visited based on the user's travel history. In this way, by considering the user's geographical location information, highly relevant information can be provided. Some or all of the above processing in the information provider may be performed using, for example, a generative AI, or without a generative AI. For example, the provider can input the user's geographical location data into a generative AI and have the generative AI perform the task of providing optimal information.

[0082] The information provider can analyze the user's social media activity and provide relevant information when providing information. For example, the provider can analyze information shared by the user on social media and provide relevant information. For example, the provider can analyze plant-related accounts followed by the user and provide relevant information. For example, the provider can provide information that the user has shown interest in on social media. In this way, relevant information can be provided by analyzing the user's social media activity. Some or all of the above processing in the information provider may be performed using, for example, a generative AI, or without a generative AI. For example, the provider can input the user's social media activity data into a generative AI and have the generative AI perform the provision of relevant information.

[0083] The notification unit can estimate the user's emotions and adjust the timing of notifications based on the estimated emotions. For example, if the user is relaxed, the notification unit will send a notification immediately. If the user is busy, the notification unit will postpone the notification. If the user is excited, the notification unit will send a notification immediately. By adjusting the timing of notifications according to the user's emotions, notifications can be sent at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0084] The notification unit can select the optimal notification method by referring to the user's past notification history when sending a notification. For example, the notification unit may prioritize notification methods that the user has received in the past. For example, the notification unit may suggest the optimal notification method based on the user's past notification history. For example, the notification unit may analyze the user's past notification history and select the optimal notification method. In this way, the optimal notification method can be selected by referring to the user's past notification history. Some or all of the above processing in the notification unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the notification unit may input the user's past notification history data into a generative AI and have the generative AI perform the selection of the optimal notification method.

[0085] The notification unit can customize notification content based on the user's current training status. For example, if the user is a beginner, the notification unit will notify them of basic training advice. If the user is an intermediate player, the notification unit will notify them of detailed training advice. If the user is an advanced player, the notification unit will notify them of expert training advice. This allows for more appropriate notifications by customizing the notification content according to the user's training status. Some or all of the above processing in the notification unit may be performed using, for example, a generative AI, or without a generative AI. For example, the notification unit can input the user's training status data into a generative AI and have the generative AI customize the notification content.

[0086] The notification unit can estimate the user's emotions and determine the priority of notifications based on the estimated emotions. For example, if the user is relaxed, the notification unit will prioritize notifications of interest. For example, if the user is busy, the notification unit will prioritize important notifications. For example, if the user is excited, the notification unit will prioritize new notifications. In this way, important notifications can be prioritized by determining the priority of notifications according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI or not using AI. For example, the notification unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0087] The notification unit can provide the most relevant notifications by considering the user's geographical location information. For example, the notification unit can provide notifications related to the user's current location. For example, the notification unit can provide notifications related to the user's past location information by analyzing it. For example, the notification unit can provide notifications related to places visited by the user based on their travel history. This allows for the provision of highly relevant notifications by considering the user's geographical location information. Some or all of the above processing in the notification unit may be performed using, for example, a generative AI, or without a generative AI. For example, the notification unit can input the user's geographical location information data into a generative AI and have the generative AI execute the most relevant notifications.

[0088] The notification unit can analyze the user's social media activity and send relevant notifications at the time of notification. For example, the notification unit can analyze information shared by the user on social media and send relevant notifications. For example, the notification unit can analyze plant-related accounts followed by the user and send relevant notifications. For example, the notification unit can send notifications based on information the user has shown interest in on social media. In this way, relevant notifications can be sent by analyzing the user's social media activity. Some or all of the above processing in the notification unit may be performed using, for example, a generative AI, or without a generative AI. For example, the notification unit can input the user's social media activity data into a generative AI and have the generative AI execute the relevant notifications.

[0089] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0090] The analysis unit can be enhanced to diagnose the health of plants when analyzing plant photographs. For example, it can detect changes in leaf color and shape to identify signs of disease or nutrient deficiency. The analysis unit can also monitor plant growth rate and detect abnormal growth patterns. For example, it can analyze spots and discoloration appearing on the plant surface to identify pest damage. By diagnosing the plant's health, it can suggest early countermeasures to the user.

[0091] The information provider can estimate the user's emotions and, based on those emotions, offer stories and anecdotes about plants. For example, if the user is relaxed, it can offer interesting stories about the history and culture of plants. If the user is busy, it can offer short trivia about plants. If the user is excited, it can offer surprising facts and discoveries about plants. By tailoring the information provided according to the user's emotions, it can deliver more engaging information.

[0092] The notification unit can send notifications to the user prompting specific actions according to the plant's growth stage. For example, it can notify the user when the plant has germinated, when it's time to water it, when it has entered its growth phase, when it needs to add fertilizer, and when it has entered its flowering phase, when it needs to care for the flowers. This allows the system to support the healthy growth of plants by prompting appropriate actions according to their growth stage.

[0093] The analysis unit can be enhanced with a function to predict plant growth when analyzing plant photographs. For example, it can predict future growth based on the current growth state and suggest appropriate cultivation methods. The analysis unit can also analyze the plant's growth rate and predict growth delays or abnormalities. For example, it can consider the plant's environmental conditions and suggest the optimal growth environment. By predicting plant growth, it can provide users with more effective cultivation methods.

[0094] The service provider can estimate the user's emotions and, based on those emotions, offer quizzes and games about plants. For example, if the user is relaxed, it can offer quizzes to deepen their knowledge of plants. If the user is busy, it can offer short, enjoyable mini-games about plants. If the user is excited, it can offer challenges and contests about plants. This allows users to deepen their knowledge of plants while having fun by adjusting the way information is presented according to their emotions.

[0095] The collection unit can adjust the timing of plant photo collection, taking into account the user's health condition. For example, if the user is unwell, the collection timing will be delayed. If the user is healthy, the collection unit will proactively suggest collection timings. If the user is tired, the collection unit will encourage collection at a time when they can relax. In this way, by adjusting the collection timing according to the user's health condition, plant photos can be collected without strain.

[0096] The analysis unit can be enhanced to perform plant component analysis when analyzing plant photographs. For example, it can analyze the components of plant leaves and flowers to identify nutritional value and medicinal components. The analysis unit can also analyze the components of plant roots and stems to evaluate soil conditions and nutrient balance. Furthermore, it can analyze the components of plant fruits to assess their suitability for consumption. By performing plant component analysis, the system can provide users with detailed information on how to use and cultivate plants.

[0097] The service provider can estimate the user's emotions and, based on those emotions, provide relaxation content related to plants. For example, if the user is relaxed, it can provide beautiful photos and videos of plants. If the user is busy, it can provide content combining short relaxation music with photos of plants. If the user is excited, it can provide a time-lapse video showing the growth process of a plant. In this way, by providing relaxation content according to the user's emotions, it is possible to provide an environment where users can relax through plants.

[0098] The notification unit can be enhanced to notify users of important events related to plant growth. For example, it can notify users when a plant flowers. It can also notify users when a plant bears fruit, indicating the right time for harvest. Furthermore, it can notify users of early intervention when a plant becomes diseased. By notifying users of important events related to plant growth, the system can encourage timely action.

[0099] The service provider can estimate the user's emotions and, based on those emotions, provide community features related to plants. For example, if the user is relaxed, it can provide a forum or chat room about plants. If the user is busy, it can provide a simple question and answer board about plants. If the user is excited, it can provide information about contests and events related to plants. By providing community features according to the user's emotions, it can promote the exchange of information and interaction about plants.

[0100] The following briefly describes the processing flow for example form 2.

[0101] Step 1: The collection unit collects photos of plants taken by the user. For example, it can collect photos of plants taken by the user with their smartphone or camera. By simply taking a picture of a flower the user finds in their garden, they can find out the name of the flower and how to grow it. No special operations are required; just take a picture. Step 2: The analysis unit analyzes the photos collected by the collection unit to identify information such as the plant's name, growth conditions, and cultivation methods. For example, it uses image analysis AI to analyze the characteristics of the plants shown in the collected photos. It analyzes the shape and color of the leaves, the shape of the flowers, etc., to identify specific plants. Step 3: The provisioning unit provides the user with the information identified by the analysis unit. For example, once the analysis is complete, the user is provided with detailed information. The plant's name, growth conditions, and cultivation methods are displayed, allowing the user to receive specific advice on plant cultivation. Step 4: The notification section provides seasonal plant care advice and notifications about the optimal harvest time for vegetables. For example, it provides seasonal plant care advice and notifications about the optimal harvest time for vegetables. Users can learn the appropriate timing for watering and fertilizing.

[0102] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0103] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0104] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0105] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, and notification unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit takes a photograph of a plant using the camera 42 of the smart device 14 and the control unit 46A collects the photograph. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12, which uses image analysis AI to analyze the characteristics of the plant and identify information such as its name, growth conditions, and cultivation methods. The provision unit displays the information to the user using the display 40A of the smart device 14. The notification unit is implemented in the identification processing unit 290 of the data processing unit 12, which provides seasonal plant management advice and notifications of the optimal harvest time for vegetables. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0106] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0107] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0109] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0110] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0112] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0113] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0114] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0115] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0116] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0117] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0120] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0121] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, and notification unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit takes pictures of plants using the camera 42 of the smart glasses 214 and the control unit 46A collects those pictures. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12, which uses image analysis AI to analyze the characteristics of the plants and identify information such as their name, growth conditions, and cultivation methods. The provision unit displays the information to the user using the display of the smart glasses 214. The notification unit is implemented in the identification processing unit 290 of the data processing unit 12, which provides seasonal plant management advice and notifications of the optimal harvest time for vegetables. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0122] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0123] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0124] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0125] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0126] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0128] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0129] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0130] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0131] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0132] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0133] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0134] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0136] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0137] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, and notification unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit takes pictures of plants using the camera 42 of the headset terminal 314 and the control unit 46A collects those pictures. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and uses image analysis AI to analyze the characteristics of the plants and identify information such as their name, growth conditions, and cultivation methods. The provision unit displays the information to the user using the display of the headset terminal 314. The notification unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and provides seasonal plant management advice and notifications of the optimal harvest time for vegetables. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0138] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0139] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0140] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0141] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0142] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0144] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0145] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0146] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0147] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0148] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0149] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0150] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0151] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0152] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0153] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0154] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, and notification unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the collection unit takes pictures of plants using the camera 42 of the robot 414 and the control unit 46A collects those pictures. The analysis unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12, which uses image analysis AI to analyze the characteristics of the plants and identify information such as their name, growth conditions, and cultivation methods. The provision unit displays the information to the user using the display of the robot 414. The notification unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12, which provides seasonal plant management advice and notifications of the optimal harvest time for vegetables. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0155] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0156] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0157] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0158] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0159] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0160] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0161] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0162] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0163] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0164] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0165] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0166] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0167] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0168] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0169] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0170] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0171] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0172] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0173] (Note 1) A collection unit that collects photos of plants taken by users, An analysis unit analyzes the photographs collected by the aforementioned collection unit to identify information such as the name of the plant, growth conditions, and cultivation methods. A provisioning unit that provides the user with the information identified by the analysis unit, It includes a notification unit that provides seasonal plant care advice and notifications about the optimal harvest time for vegetables. A system characterized by the following features. (Note 2) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of plant photo collection based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is The system analyzes the user's past plant photo collection history and selects the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is When collecting plant photos, filtering is performed based on the user's current areas of interest and cultivation status. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is It estimates the user's emotions and prioritizes the plant photos to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is When collecting plant photos, the system prioritizes collecting photos of plants that are highly relevant to the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is When collecting plant photos, we analyze users' social media activity and collect relevant plant photos. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, It estimates the user's emotions and adjusts the way the analysis results are presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the plants. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the plant category. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the plants were photographed. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, we refer to relevant plant literature to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned supply unit is, It estimates the user's emotions and adjusts the way information is delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned supply unit is, When providing information, the system selects the most suitable method of information delivery by referring to the user's past usage history. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned supply unit is, When providing information, adjust the level of detail based on the user's current development status. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned supply unit is, The system estimates the user's emotions and prioritizes information provision based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, When providing information, we will consider the user's geographical location to provide the most relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When providing information, we analyze the user's social media activity and provide relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned notification unit, It estimates the user's emotions and adjusts the timing of notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned notification unit, When sending a notification, the system will refer to the user's past notification history to select the most suitable notification method. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned notification unit, When sending notifications, customize the content based on the user's current progress. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned notification unit, It estimates the user's emotions and prioritizes notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned notification unit, When sending notifications, the system takes the user's geographical location into consideration to provide the most appropriate notification. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned notification unit, When sending notifications, the system analyzes the user's social media activity to provide relevant notifications. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0174] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects photos of plants taken by users, An analysis unit analyzes the photographs collected by the aforementioned collection unit to identify information such as the name of the plant, growth conditions, and cultivation methods. A provisioning unit that provides the user with the information identified by the analysis unit, It includes a notification unit that provides seasonal plant care advice and notifications about the optimal harvest time for vegetables. A system characterized by the following features.

2. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of plant photo collection based on those emotions. The system according to feature 1.

3. The aforementioned collection unit is The system analyzes the user's past plant photo collection history and selects the optimal collection method. The system according to feature 1.

4. The aforementioned collection unit is When collecting plant photos, filtering is performed based on the user's current areas of interest and cultivation status. The system according to feature 1.

5. The aforementioned collection unit is It estimates the user's emotions and prioritizes the plant photos to collect based on those estimated emotions. The system according to feature 1.

6. The aforementioned collection unit is When collecting plant photos, the system prioritizes collecting photos of plants that are highly relevant to the user's geographical location. The system according to feature 1.

7. The aforementioned collection unit is When collecting plant photos, we analyze users' social media activity and collect relevant plant photos. The system according to feature 1.

8. The aforementioned analysis unit, It estimates the user's emotions and adjusts the way the analysis results are presented based on the estimated user emotions. The system according to feature 1.

9. The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the plants. The system according to feature 1.

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